I Built a Java + Spring Boot Lab to Understand What Actually Happens in Production

Alok Pandey — Sun, 27 Sep 2026 19:26:17 +0000

I Built a Java + Spring Boot Lab to Understand What Actually Happens in Production

I've spent a lot of time preparing for Java and Spring interviews.

And I noticed something.

A lot of preparation eventually becomes:

"What is @Transactional?"

"What is dependency injection?"

"What is ConcurrentHashMap?"

"What is the difference between offset and cursor pagination?"

You can memorize the answers.

But then someone asks:

"Okay. Show me what actually happens."

That's where things get interesting.

So I built a runnable Java + Spring Boot learning lab around exactly that idea:

Understand → Run → Break → Observe → Fix → Explain

GitHub: https://github.com/wildoctopus/spring-enterprise-learning-lab

What is the Spring Enterprise Learning Lab?

It's a Java 21 + Spring Boot playground focused on understanding the concepts that actually matter when building and debugging enterprise applications.

It covers areas including:

Core Java
Collections
Concurrency
JVM internals
Spring Bean lifecycle
Dependency Injection
Spring proxies
Transactions
JPA/Hibernate
SQL
REST
Pagination
Streams
Caching
System design
Production-oriented scenarios

The goal isn't another collection of theoretical notes.

The goal is to make the concepts runnable and observable.

Don't just read it. Break it.

A lot of technical learning looks like this:

Read → Understand → Forget → Read again.

I wanted something different:

Understand → Run → Break → Observe → Fix → Explain

Because sometimes the fastest way to understand a concept is to deliberately make it fail.

What REALLY happens when Spring Boot starts?

For example, instead of simply memorizing:

"Spring creates beans and injects dependencies."

I wanted to understand the actual lifecycle:

Application starts

↓

ApplicationContext

↓

Bean definitions

↓

Bean creation

↓

Dependency injection

↓

BeanPostProcessor

↓

Initialization callbacks

↓

Proxies

↓

Ready for use

And then the questions become much more interesting:

When exactly is a bean created?
When does dependency injection happen?
Where do BeanPostProcessors fit?
When does @PostConstruct run?
When are proxies involved?
Why does self-invocation matter?
What actually happens with @Transactional?

The repository contains runnable examples and tests to explore these behaviors.

`@Transactional` isn't magic

It's easy to learn:

@Transactional = transaction.

But production systems make you ask:

Who actually starts the transaction?
How does Spring intercept the method?
Where does the proxy come into the picture?
What happens during self-invocation?
When does rollback happen?
What happens with exceptions?
What happens when database operations become slow?

Understanding the mechanism is much more useful than memorizing the annotation.

Concurrency: go beyond "ConcurrentHashMap is thread-safe"

The same approach applies to concurrency.

Instead of stopping at:

"ConcurrentHashMap is thread-safe."

you can investigate:

Concurrent updates
Atomic operations
putIfAbsent
compute
merge
Executor behavior
Immutable objects
Race conditions
Singleton failure scenarios

The interesting interview question isn't always:

"What is ConcurrentHashMap?"

It can be:

"What happens when multiple threads modify the same data?"

JVM internals without just memorizing diagrams

We've all seen:

Heap

Stack

Metaspace

Eden

Survivor

Old Generation

But knowing the terminology is only the beginning.

The interesting part is connecting:

Java code

↓

Objects

↓

Memory

↓

Garbage Collection

↓

JVM behavior

↓

Production consequences

The JVM playground is designed to help make that connection.

Offset vs Cursor Pagination

Another example is pagination.

Most developers know:

LIMIT 20 OFFSET 10000

But what happens when the dataset becomes very large?

What happens when records are inserted or deleted while users are navigating through pages?

What happens when the API needs to support large datasets efficiently?

The repository includes pagination experiments around offset and cursor approaches, including tenant-bound cursor validation.

Because pagination isn't just about syntax.

It's about:

Performance
Consistency
Correctness
Security
API design
Scalability

The idea behind the repository

Senior Java discussions often move beyond:

"What is this API?"

towards:

"Why does it work?"

"How does it work internally?"

"What happens when it fails?"

"What happens under concurrency?"

"What happens under load?"

"What trade-off would you make?"

That's the type of thinking I want this repository to encourage.

Not just:

"What is Spring Boot?"

But:

"What is Spring Boot actually doing?"

Who is this for?

This can be useful if you're:

Preparing for Java backend interviews
Preparing for Senior/Lead Java roles
Learning Spring Boot beyond annotations
Moving toward senior engineering
Exploring JVM internals
Learning JPA/Hibernate
Practicing concurrency
Learning production-oriented system design
Looking for runnable examples instead of theory alone

You don't need to study everything at once.

Pick one topic.

Run it.

Break it.

Understand it.

Move to the next one.

Getting started

You'll need Java 21+ and Maven.

Clone the repository:

git clone https://github.com/wildoctopus/spring-enterprise-learning-lab.git

Then:

cd spring-enterprise-learning-lab

Run the tests:

mvn clean test

Or start the application:

mvn spring-boot:run

Then explore one of the available playgrounds.

What's next?

The lab is still evolving.

I'd like to expand it with more experiments around:

Spring internals
JVM behavior
Database performance
Kafka and event-driven systems
Distributed systems
Observability
Resilience
Caching
Production failure scenarios
System design
Advanced concurrency

The philosophy will stay the same:

Don't just read about the problem. Reproduce it.

Your turn

If you work with Java or Spring in production:

What's one Java/Spring concept you only truly understood after something broke?

Drop it in the comments.

It might become the next experiment in the lab.

And if you find the repository useful, a ⭐ on GitHub helps other developers discover it.

GitHub: https://github.com/wildoctopus/spring-enterprise-learning-lab

Happy breaking things. 😄

Deep Learning Training Playbook: Turning Research Papers into Engineering Decisions

Alok Pandey — Mon, 03 Aug 2026 12:49:37 +0000

Deep Learning is evolving faster than ever.

Every week we see new techniques:

LoRA
QLoRA
RAG
Foundation Models
Mamba
Diffusion Models

But one question is still difficult:

Which technique should I choose for my problem?

Most resources explain how something works.

Very few explain:

Why was it created?
When should I use it?
When should I avoid it?
What are the trade-offs?

That is the problem I wanted to solve.

Introducing: Deep Learning Training Playbook

A practical guide to help engineers make better AI decisions.

The first part covers:

✅ Transfer Learning

✅ Foundation Models

✅ Full Fine-Tuning

✅ LoRA

✅ QLoRA

✅ Continued Pretraining

✅ Knowledge Distillation

The goal:

Don't memorize AI techniques. Learn when and why to use them.

Open Source

GitHub:

https://github.com/wildoctopus/deep-learning-training-playbook

More chapters coming:

Model Architectures
Generative AI
Training Optimization
Agentic AI
Production AI Systems

Built by WildOctopus 🐙

Turning research papers into practical engineering playbooks.

DEV Community: Alok Pandey

I Built a Java + Spring Boot Lab to Understand What Actually Happens in Production

I Built a Java + Spring Boot Lab to Understand What Actually Happens in Production

What is the Spring Enterprise Learning Lab?

Don't just read it. Break it.

What REALLY happens when Spring Boot starts?

@Transactional isn't magic

Concurrency: go beyond "ConcurrentHashMap is thread-safe"

JVM internals without just memorizing diagrams

Offset vs Cursor Pagination

The idea behind the repository

Who is this for?

Getting started

What's next?

Your turn

Deep Learning Training Playbook: Turning Research Papers into Engineering Decisions

Introducing: Deep Learning Training Playbook

Open Source

`@Transactional` isn't magic